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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 12 Dec 2017 14:46:22 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/12/t1513087678pkm55tdrs0ruzqt.htm/, Retrieved Wed, 15 May 2024 07:37:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309104, Retrieved Wed, 15 May 2024 07:37:09 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple Regression] [2017-12-12 13:46:22] [da16992a3cc08a6ca750b2899511bd69] [Current]
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Dataseries X:
1	10,7	1
1	15,5	0
1	11,9	0
1	9,3	1
0	8,3	0
0	8,6	0
0	5,7	0
0	6,6	1
1	15,3	1
1	14,8	1
0	4,2	0
1	11,8	0
0	7,4	0
1	9,4	0
1	5	0
1	10,1	0
1	6	1
1	11,9	1
0	9,9	0
0	7	1
0	2,9	0
1	6,3	0
0	4,8	0
1	13,9	1
1	10,5	0
1	9,8	0
1	9,9	0
0	13,1	0
0	7,1	0
0	9,2	0
0	10,8	0
0	7	0
1	12,2	1
1	8,1	0
1	6,6	0
1	16,1	1
0	7,3	0
1	6,7	0
0	5,1	0
1	11,9	1
1	10,3	0
1	10,6	1
1	18,6	1
1	9,7	0
0	4,5	0
0	10,1	1
0	6,9	0
1	6,5	1
1	6,2	0
1	13,4	0




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309104&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]8 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309104&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309104&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R ServerBig Analytics Cloud Computing Center







Multiple Linear Regression - Estimated Regression Equation
WithoutHealthInsurance[t] = + 7.00294 + 2.7Politiek[t] + 2.14706DeelVanDeSouth[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WithoutHealthInsurance[t] =  +  7.00294 +  2.7Politiek[t] +  2.14706DeelVanDeSouth[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309104&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WithoutHealthInsurance[t] =  +  7.00294 +  2.7Politiek[t] +  2.14706DeelVanDeSouth[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309104&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309104&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
WithoutHealthInsurance[t] = + 7.00294 + 2.7Politiek[t] + 2.14706DeelVanDeSouth[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+7.003 0.6738+1.0390e+01 9.085e-14 4.542e-14
Politiek+2.7 0.8911+3.0300e+00 0.003969 0.001984
DeelVanDeSouth+2.147 0.9359+2.2940e+00 0.0263 0.01315

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +7.003 &  0.6738 & +1.0390e+01 &  9.085e-14 &  4.542e-14 \tabularnewline
Politiek & +2.7 &  0.8911 & +3.0300e+00 &  0.003969 &  0.001984 \tabularnewline
DeelVanDeSouth & +2.147 &  0.9359 & +2.2940e+00 &  0.0263 &  0.01315 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309104&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+7.003[/C][C] 0.6738[/C][C]+1.0390e+01[/C][C] 9.085e-14[/C][C] 4.542e-14[/C][/ROW]
[ROW][C]Politiek[/C][C]+2.7[/C][C] 0.8911[/C][C]+3.0300e+00[/C][C] 0.003969[/C][C] 0.001984[/C][/ROW]
[ROW][C]DeelVanDeSouth[/C][C]+2.147[/C][C] 0.9359[/C][C]+2.2940e+00[/C][C] 0.0263[/C][C] 0.01315[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309104&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309104&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+7.003 0.6738+1.0390e+01 9.085e-14 4.542e-14
Politiek+2.7 0.8911+3.0300e+00 0.003969 0.001984
DeelVanDeSouth+2.147 0.9359+2.2940e+00 0.0263 0.01315







Multiple Linear Regression - Regression Statistics
Multiple R 0.55
R-squared 0.3025
Adjusted R-squared 0.2728
F-TEST (value) 10.19
F-TEST (DF numerator)2
F-TEST (DF denominator)47
p-value 0.0002104
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.947
Sum Squared Residuals 408.2

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.55 \tabularnewline
R-squared &  0.3025 \tabularnewline
Adjusted R-squared &  0.2728 \tabularnewline
F-TEST (value) &  10.19 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value &  0.0002104 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.947 \tabularnewline
Sum Squared Residuals &  408.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309104&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.55[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3025[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2728[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 10.19[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C] 0.0002104[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.947[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 408.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309104&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309104&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R 0.55
R-squared 0.3025
Adjusted R-squared 0.2728
F-TEST (value) 10.19
F-TEST (DF numerator)2
F-TEST (DF denominator)47
p-value 0.0002104
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.947
Sum Squared Residuals 408.2







Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309104&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309104&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309104&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 10.7 11.85-1.15
2 15.5 9.703 5.797
3 11.9 9.703 2.197
4 9.3 11.85-2.55
5 8.3 7.003 1.297
6 8.6 7.003 1.597
7 5.7 7.003-1.303
8 6.6 9.15-2.55
9 15.3 11.85 3.45
10 14.8 11.85 2.95
11 4.2 7.003-2.803
12 11.8 9.703 2.097
13 7.4 7.003 0.3971
14 9.4 9.703-0.3029
15 5 9.703-4.703
16 10.1 9.703 0.3971
17 6 11.85-5.85
18 11.9 11.85 0.05
19 9.9 7.003 2.897
20 7 9.15-2.15
21 2.9 7.003-4.103
22 6.3 9.703-3.403
23 4.8 7.003-2.203
24 13.9 11.85 2.05
25 10.5 9.703 0.7971
26 9.8 9.703 0.09706
27 9.9 9.703 0.1971
28 13.1 7.003 6.097
29 7.1 7.003 0.09706
30 9.2 7.003 2.197
31 10.8 7.003 3.797
32 7 7.003-0.002941
33 12.2 11.85 0.35
34 8.1 9.703-1.603
35 6.6 9.703-3.103
36 16.1 11.85 4.25
37 7.3 7.003 0.2971
38 6.7 9.703-3.003
39 5.1 7.003-1.903
40 11.9 11.85 0.05
41 10.3 9.703 0.5971
42 10.6 11.85-1.25
43 18.6 11.85 6.75
44 9.7 9.703-0.002941
45 4.5 7.003-2.503
46 10.1 9.15 0.95
47 6.9 7.003-0.1029
48 6.5 11.85-5.35
49 6.2 9.703-3.503
50 13.4 9.703 3.697

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10.7 &  11.85 & -1.15 \tabularnewline
2 &  15.5 &  9.703 &  5.797 \tabularnewline
3 &  11.9 &  9.703 &  2.197 \tabularnewline
4 &  9.3 &  11.85 & -2.55 \tabularnewline
5 &  8.3 &  7.003 &  1.297 \tabularnewline
6 &  8.6 &  7.003 &  1.597 \tabularnewline
7 &  5.7 &  7.003 & -1.303 \tabularnewline
8 &  6.6 &  9.15 & -2.55 \tabularnewline
9 &  15.3 &  11.85 &  3.45 \tabularnewline
10 &  14.8 &  11.85 &  2.95 \tabularnewline
11 &  4.2 &  7.003 & -2.803 \tabularnewline
12 &  11.8 &  9.703 &  2.097 \tabularnewline
13 &  7.4 &  7.003 &  0.3971 \tabularnewline
14 &  9.4 &  9.703 & -0.3029 \tabularnewline
15 &  5 &  9.703 & -4.703 \tabularnewline
16 &  10.1 &  9.703 &  0.3971 \tabularnewline
17 &  6 &  11.85 & -5.85 \tabularnewline
18 &  11.9 &  11.85 &  0.05 \tabularnewline
19 &  9.9 &  7.003 &  2.897 \tabularnewline
20 &  7 &  9.15 & -2.15 \tabularnewline
21 &  2.9 &  7.003 & -4.103 \tabularnewline
22 &  6.3 &  9.703 & -3.403 \tabularnewline
23 &  4.8 &  7.003 & -2.203 \tabularnewline
24 &  13.9 &  11.85 &  2.05 \tabularnewline
25 &  10.5 &  9.703 &  0.7971 \tabularnewline
26 &  9.8 &  9.703 &  0.09706 \tabularnewline
27 &  9.9 &  9.703 &  0.1971 \tabularnewline
28 &  13.1 &  7.003 &  6.097 \tabularnewline
29 &  7.1 &  7.003 &  0.09706 \tabularnewline
30 &  9.2 &  7.003 &  2.197 \tabularnewline
31 &  10.8 &  7.003 &  3.797 \tabularnewline
32 &  7 &  7.003 & -0.002941 \tabularnewline
33 &  12.2 &  11.85 &  0.35 \tabularnewline
34 &  8.1 &  9.703 & -1.603 \tabularnewline
35 &  6.6 &  9.703 & -3.103 \tabularnewline
36 &  16.1 &  11.85 &  4.25 \tabularnewline
37 &  7.3 &  7.003 &  0.2971 \tabularnewline
38 &  6.7 &  9.703 & -3.003 \tabularnewline
39 &  5.1 &  7.003 & -1.903 \tabularnewline
40 &  11.9 &  11.85 &  0.05 \tabularnewline
41 &  10.3 &  9.703 &  0.5971 \tabularnewline
42 &  10.6 &  11.85 & -1.25 \tabularnewline
43 &  18.6 &  11.85 &  6.75 \tabularnewline
44 &  9.7 &  9.703 & -0.002941 \tabularnewline
45 &  4.5 &  7.003 & -2.503 \tabularnewline
46 &  10.1 &  9.15 &  0.95 \tabularnewline
47 &  6.9 &  7.003 & -0.1029 \tabularnewline
48 &  6.5 &  11.85 & -5.35 \tabularnewline
49 &  6.2 &  9.703 & -3.503 \tabularnewline
50 &  13.4 &  9.703 &  3.697 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309104&T=5

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 10.7[/C][C] 11.85[/C][C]-1.15[/C][/ROW]
[ROW][C]2[/C][C] 15.5[/C][C] 9.703[/C][C] 5.797[/C][/ROW]
[ROW][C]3[/C][C] 11.9[/C][C] 9.703[/C][C] 2.197[/C][/ROW]
[ROW][C]4[/C][C] 9.3[/C][C] 11.85[/C][C]-2.55[/C][/ROW]
[ROW][C]5[/C][C] 8.3[/C][C] 7.003[/C][C] 1.297[/C][/ROW]
[ROW][C]6[/C][C] 8.6[/C][C] 7.003[/C][C] 1.597[/C][/ROW]
[ROW][C]7[/C][C] 5.7[/C][C] 7.003[/C][C]-1.303[/C][/ROW]
[ROW][C]8[/C][C] 6.6[/C][C] 9.15[/C][C]-2.55[/C][/ROW]
[ROW][C]9[/C][C] 15.3[/C][C] 11.85[/C][C] 3.45[/C][/ROW]
[ROW][C]10[/C][C] 14.8[/C][C] 11.85[/C][C] 2.95[/C][/ROW]
[ROW][C]11[/C][C] 4.2[/C][C] 7.003[/C][C]-2.803[/C][/ROW]
[ROW][C]12[/C][C] 11.8[/C][C] 9.703[/C][C] 2.097[/C][/ROW]
[ROW][C]13[/C][C] 7.4[/C][C] 7.003[/C][C] 0.3971[/C][/ROW]
[ROW][C]14[/C][C] 9.4[/C][C] 9.703[/C][C]-0.3029[/C][/ROW]
[ROW][C]15[/C][C] 5[/C][C] 9.703[/C][C]-4.703[/C][/ROW]
[ROW][C]16[/C][C] 10.1[/C][C] 9.703[/C][C] 0.3971[/C][/ROW]
[ROW][C]17[/C][C] 6[/C][C] 11.85[/C][C]-5.85[/C][/ROW]
[ROW][C]18[/C][C] 11.9[/C][C] 11.85[/C][C] 0.05[/C][/ROW]
[ROW][C]19[/C][C] 9.9[/C][C] 7.003[/C][C] 2.897[/C][/ROW]
[ROW][C]20[/C][C] 7[/C][C] 9.15[/C][C]-2.15[/C][/ROW]
[ROW][C]21[/C][C] 2.9[/C][C] 7.003[/C][C]-4.103[/C][/ROW]
[ROW][C]22[/C][C] 6.3[/C][C] 9.703[/C][C]-3.403[/C][/ROW]
[ROW][C]23[/C][C] 4.8[/C][C] 7.003[/C][C]-2.203[/C][/ROW]
[ROW][C]24[/C][C] 13.9[/C][C] 11.85[/C][C] 2.05[/C][/ROW]
[ROW][C]25[/C][C] 10.5[/C][C] 9.703[/C][C] 0.7971[/C][/ROW]
[ROW][C]26[/C][C] 9.8[/C][C] 9.703[/C][C] 0.09706[/C][/ROW]
[ROW][C]27[/C][C] 9.9[/C][C] 9.703[/C][C] 0.1971[/C][/ROW]
[ROW][C]28[/C][C] 13.1[/C][C] 7.003[/C][C] 6.097[/C][/ROW]
[ROW][C]29[/C][C] 7.1[/C][C] 7.003[/C][C] 0.09706[/C][/ROW]
[ROW][C]30[/C][C] 9.2[/C][C] 7.003[/C][C] 2.197[/C][/ROW]
[ROW][C]31[/C][C] 10.8[/C][C] 7.003[/C][C] 3.797[/C][/ROW]
[ROW][C]32[/C][C] 7[/C][C] 7.003[/C][C]-0.002941[/C][/ROW]
[ROW][C]33[/C][C] 12.2[/C][C] 11.85[/C][C] 0.35[/C][/ROW]
[ROW][C]34[/C][C] 8.1[/C][C] 9.703[/C][C]-1.603[/C][/ROW]
[ROW][C]35[/C][C] 6.6[/C][C] 9.703[/C][C]-3.103[/C][/ROW]
[ROW][C]36[/C][C] 16.1[/C][C] 11.85[/C][C] 4.25[/C][/ROW]
[ROW][C]37[/C][C] 7.3[/C][C] 7.003[/C][C] 0.2971[/C][/ROW]
[ROW][C]38[/C][C] 6.7[/C][C] 9.703[/C][C]-3.003[/C][/ROW]
[ROW][C]39[/C][C] 5.1[/C][C] 7.003[/C][C]-1.903[/C][/ROW]
[ROW][C]40[/C][C] 11.9[/C][C] 11.85[/C][C] 0.05[/C][/ROW]
[ROW][C]41[/C][C] 10.3[/C][C] 9.703[/C][C] 0.5971[/C][/ROW]
[ROW][C]42[/C][C] 10.6[/C][C] 11.85[/C][C]-1.25[/C][/ROW]
[ROW][C]43[/C][C] 18.6[/C][C] 11.85[/C][C] 6.75[/C][/ROW]
[ROW][C]44[/C][C] 9.7[/C][C] 9.703[/C][C]-0.002941[/C][/ROW]
[ROW][C]45[/C][C] 4.5[/C][C] 7.003[/C][C]-2.503[/C][/ROW]
[ROW][C]46[/C][C] 10.1[/C][C] 9.15[/C][C] 0.95[/C][/ROW]
[ROW][C]47[/C][C] 6.9[/C][C] 7.003[/C][C]-0.1029[/C][/ROW]
[ROW][C]48[/C][C] 6.5[/C][C] 11.85[/C][C]-5.35[/C][/ROW]
[ROW][C]49[/C][C] 6.2[/C][C] 9.703[/C][C]-3.503[/C][/ROW]
[ROW][C]50[/C][C] 13.4[/C][C] 9.703[/C][C] 3.697[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309104&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309104&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 10.7 11.85-1.15
2 15.5 9.703 5.797
3 11.9 9.703 2.197
4 9.3 11.85-2.55
5 8.3 7.003 1.297
6 8.6 7.003 1.597
7 5.7 7.003-1.303
8 6.6 9.15-2.55
9 15.3 11.85 3.45
10 14.8 11.85 2.95
11 4.2 7.003-2.803
12 11.8 9.703 2.097
13 7.4 7.003 0.3971
14 9.4 9.703-0.3029
15 5 9.703-4.703
16 10.1 9.703 0.3971
17 6 11.85-5.85
18 11.9 11.85 0.05
19 9.9 7.003 2.897
20 7 9.15-2.15
21 2.9 7.003-4.103
22 6.3 9.703-3.403
23 4.8 7.003-2.203
24 13.9 11.85 2.05
25 10.5 9.703 0.7971
26 9.8 9.703 0.09706
27 9.9 9.703 0.1971
28 13.1 7.003 6.097
29 7.1 7.003 0.09706
30 9.2 7.003 2.197
31 10.8 7.003 3.797
32 7 7.003-0.002941
33 12.2 11.85 0.35
34 8.1 9.703-1.603
35 6.6 9.703-3.103
36 16.1 11.85 4.25
37 7.3 7.003 0.2971
38 6.7 9.703-3.003
39 5.1 7.003-1.903
40 11.9 11.85 0.05
41 10.3 9.703 0.5971
42 10.6 11.85-1.25
43 18.6 11.85 6.75
44 9.7 9.703-0.002941
45 4.5 7.003-2.503
46 10.1 9.15 0.95
47 6.9 7.003-0.1029
48 6.5 11.85-5.35
49 6.2 9.703-3.503
50 13.4 9.703 3.697







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6 0.1683 0.3367 0.8317
7 0.1624 0.3249 0.8376
8 0.1287 0.2574 0.8713
9 0.2773 0.5546 0.7227
10 0.2694 0.5388 0.7306
11 0.2901 0.5802 0.7099
12 0.2307 0.4613 0.7693
13 0.157 0.3141 0.843
14 0.173 0.346 0.827
15 0.4845 0.969 0.5155
16 0.396 0.7921 0.604
17 0.6373 0.7253 0.3627
18 0.5519 0.8961 0.4481
19 0.5462 0.9076 0.4538
20 0.5035 0.9931 0.4965
21 0.5862 0.8275 0.4138
22 0.6247 0.7507 0.3753
23 0.5892 0.8217 0.4108
24 0.5446 0.9109 0.4554
25 0.467 0.9339 0.533
26 0.3855 0.7709 0.6145
27 0.3097 0.6194 0.6903
28 0.5673 0.8654 0.4327
29 0.4798 0.9596 0.5202
30 0.4374 0.8747 0.5626
31 0.5063 0.9873 0.4937
32 0.422 0.8441 0.578
33 0.3387 0.6775 0.6613
34 0.2735 0.5469 0.7265
35 0.2578 0.5157 0.7422
36 0.3049 0.6099 0.6951
37 0.2317 0.4634 0.7683
38 0.2108 0.4217 0.7892
39 0.153 0.306 0.847
40 0.09698 0.194 0.903
41 0.05762 0.1152 0.9424
42 0.0356 0.07119 0.9644
43 0.2397 0.4793 0.7603
44 0.1407 0.2814 0.8593

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.1683 &  0.3367 &  0.8317 \tabularnewline
7 &  0.1624 &  0.3249 &  0.8376 \tabularnewline
8 &  0.1287 &  0.2574 &  0.8713 \tabularnewline
9 &  0.2773 &  0.5546 &  0.7227 \tabularnewline
10 &  0.2694 &  0.5388 &  0.7306 \tabularnewline
11 &  0.2901 &  0.5802 &  0.7099 \tabularnewline
12 &  0.2307 &  0.4613 &  0.7693 \tabularnewline
13 &  0.157 &  0.3141 &  0.843 \tabularnewline
14 &  0.173 &  0.346 &  0.827 \tabularnewline
15 &  0.4845 &  0.969 &  0.5155 \tabularnewline
16 &  0.396 &  0.7921 &  0.604 \tabularnewline
17 &  0.6373 &  0.7253 &  0.3627 \tabularnewline
18 &  0.5519 &  0.8961 &  0.4481 \tabularnewline
19 &  0.5462 &  0.9076 &  0.4538 \tabularnewline
20 &  0.5035 &  0.9931 &  0.4965 \tabularnewline
21 &  0.5862 &  0.8275 &  0.4138 \tabularnewline
22 &  0.6247 &  0.7507 &  0.3753 \tabularnewline
23 &  0.5892 &  0.8217 &  0.4108 \tabularnewline
24 &  0.5446 &  0.9109 &  0.4554 \tabularnewline
25 &  0.467 &  0.9339 &  0.533 \tabularnewline
26 &  0.3855 &  0.7709 &  0.6145 \tabularnewline
27 &  0.3097 &  0.6194 &  0.6903 \tabularnewline
28 &  0.5673 &  0.8654 &  0.4327 \tabularnewline
29 &  0.4798 &  0.9596 &  0.5202 \tabularnewline
30 &  0.4374 &  0.8747 &  0.5626 \tabularnewline
31 &  0.5063 &  0.9873 &  0.4937 \tabularnewline
32 &  0.422 &  0.8441 &  0.578 \tabularnewline
33 &  0.3387 &  0.6775 &  0.6613 \tabularnewline
34 &  0.2735 &  0.5469 &  0.7265 \tabularnewline
35 &  0.2578 &  0.5157 &  0.7422 \tabularnewline
36 &  0.3049 &  0.6099 &  0.6951 \tabularnewline
37 &  0.2317 &  0.4634 &  0.7683 \tabularnewline
38 &  0.2108 &  0.4217 &  0.7892 \tabularnewline
39 &  0.153 &  0.306 &  0.847 \tabularnewline
40 &  0.09698 &  0.194 &  0.903 \tabularnewline
41 &  0.05762 &  0.1152 &  0.9424 \tabularnewline
42 &  0.0356 &  0.07119 &  0.9644 \tabularnewline
43 &  0.2397 &  0.4793 &  0.7603 \tabularnewline
44 &  0.1407 &  0.2814 &  0.8593 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309104&T=6

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]6[/C][C] 0.1683[/C][C] 0.3367[/C][C] 0.8317[/C][/ROW]
[ROW][C]7[/C][C] 0.1624[/C][C] 0.3249[/C][C] 0.8376[/C][/ROW]
[ROW][C]8[/C][C] 0.1287[/C][C] 0.2574[/C][C] 0.8713[/C][/ROW]
[ROW][C]9[/C][C] 0.2773[/C][C] 0.5546[/C][C] 0.7227[/C][/ROW]
[ROW][C]10[/C][C] 0.2694[/C][C] 0.5388[/C][C] 0.7306[/C][/ROW]
[ROW][C]11[/C][C] 0.2901[/C][C] 0.5802[/C][C] 0.7099[/C][/ROW]
[ROW][C]12[/C][C] 0.2307[/C][C] 0.4613[/C][C] 0.7693[/C][/ROW]
[ROW][C]13[/C][C] 0.157[/C][C] 0.3141[/C][C] 0.843[/C][/ROW]
[ROW][C]14[/C][C] 0.173[/C][C] 0.346[/C][C] 0.827[/C][/ROW]
[ROW][C]15[/C][C] 0.4845[/C][C] 0.969[/C][C] 0.5155[/C][/ROW]
[ROW][C]16[/C][C] 0.396[/C][C] 0.7921[/C][C] 0.604[/C][/ROW]
[ROW][C]17[/C][C] 0.6373[/C][C] 0.7253[/C][C] 0.3627[/C][/ROW]
[ROW][C]18[/C][C] 0.5519[/C][C] 0.8961[/C][C] 0.4481[/C][/ROW]
[ROW][C]19[/C][C] 0.5462[/C][C] 0.9076[/C][C] 0.4538[/C][/ROW]
[ROW][C]20[/C][C] 0.5035[/C][C] 0.9931[/C][C] 0.4965[/C][/ROW]
[ROW][C]21[/C][C] 0.5862[/C][C] 0.8275[/C][C] 0.4138[/C][/ROW]
[ROW][C]22[/C][C] 0.6247[/C][C] 0.7507[/C][C] 0.3753[/C][/ROW]
[ROW][C]23[/C][C] 0.5892[/C][C] 0.8217[/C][C] 0.4108[/C][/ROW]
[ROW][C]24[/C][C] 0.5446[/C][C] 0.9109[/C][C] 0.4554[/C][/ROW]
[ROW][C]25[/C][C] 0.467[/C][C] 0.9339[/C][C] 0.533[/C][/ROW]
[ROW][C]26[/C][C] 0.3855[/C][C] 0.7709[/C][C] 0.6145[/C][/ROW]
[ROW][C]27[/C][C] 0.3097[/C][C] 0.6194[/C][C] 0.6903[/C][/ROW]
[ROW][C]28[/C][C] 0.5673[/C][C] 0.8654[/C][C] 0.4327[/C][/ROW]
[ROW][C]29[/C][C] 0.4798[/C][C] 0.9596[/C][C] 0.5202[/C][/ROW]
[ROW][C]30[/C][C] 0.4374[/C][C] 0.8747[/C][C] 0.5626[/C][/ROW]
[ROW][C]31[/C][C] 0.5063[/C][C] 0.9873[/C][C] 0.4937[/C][/ROW]
[ROW][C]32[/C][C] 0.422[/C][C] 0.8441[/C][C] 0.578[/C][/ROW]
[ROW][C]33[/C][C] 0.3387[/C][C] 0.6775[/C][C] 0.6613[/C][/ROW]
[ROW][C]34[/C][C] 0.2735[/C][C] 0.5469[/C][C] 0.7265[/C][/ROW]
[ROW][C]35[/C][C] 0.2578[/C][C] 0.5157[/C][C] 0.7422[/C][/ROW]
[ROW][C]36[/C][C] 0.3049[/C][C] 0.6099[/C][C] 0.6951[/C][/ROW]
[ROW][C]37[/C][C] 0.2317[/C][C] 0.4634[/C][C] 0.7683[/C][/ROW]
[ROW][C]38[/C][C] 0.2108[/C][C] 0.4217[/C][C] 0.7892[/C][/ROW]
[ROW][C]39[/C][C] 0.153[/C][C] 0.306[/C][C] 0.847[/C][/ROW]
[ROW][C]40[/C][C] 0.09698[/C][C] 0.194[/C][C] 0.903[/C][/ROW]
[ROW][C]41[/C][C] 0.05762[/C][C] 0.1152[/C][C] 0.9424[/C][/ROW]
[ROW][C]42[/C][C] 0.0356[/C][C] 0.07119[/C][C] 0.9644[/C][/ROW]
[ROW][C]43[/C][C] 0.2397[/C][C] 0.4793[/C][C] 0.7603[/C][/ROW]
[ROW][C]44[/C][C] 0.1407[/C][C] 0.2814[/C][C] 0.8593[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309104&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309104&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6 0.1683 0.3367 0.8317
7 0.1624 0.3249 0.8376
8 0.1287 0.2574 0.8713
9 0.2773 0.5546 0.7227
10 0.2694 0.5388 0.7306
11 0.2901 0.5802 0.7099
12 0.2307 0.4613 0.7693
13 0.157 0.3141 0.843
14 0.173 0.346 0.827
15 0.4845 0.969 0.5155
16 0.396 0.7921 0.604
17 0.6373 0.7253 0.3627
18 0.5519 0.8961 0.4481
19 0.5462 0.9076 0.4538
20 0.5035 0.9931 0.4965
21 0.5862 0.8275 0.4138
22 0.6247 0.7507 0.3753
23 0.5892 0.8217 0.4108
24 0.5446 0.9109 0.4554
25 0.467 0.9339 0.533
26 0.3855 0.7709 0.6145
27 0.3097 0.6194 0.6903
28 0.5673 0.8654 0.4327
29 0.4798 0.9596 0.5202
30 0.4374 0.8747 0.5626
31 0.5063 0.9873 0.4937
32 0.422 0.8441 0.578
33 0.3387 0.6775 0.6613
34 0.2735 0.5469 0.7265
35 0.2578 0.5157 0.7422
36 0.3049 0.6099 0.6951
37 0.2317 0.4634 0.7683
38 0.2108 0.4217 0.7892
39 0.153 0.306 0.847
40 0.09698 0.194 0.903
41 0.05762 0.1152 0.9424
42 0.0356 0.07119 0.9644
43 0.2397 0.4793 0.7603
44 0.1407 0.2814 0.8593







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level10.025641OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.025641 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309104&T=7

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.025641[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309104&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309104&T=7

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level10.025641OK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.41674, df1 = 2, df2 = 45, p-value = 0.6617
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0, df1 = 4, df2 = 43, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.41674, df1 = 2, df2 = 45, p-value = 0.6617

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.41674, df1 = 2, df2 = 45, p-value = 0.6617
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0, df1 = 4, df2 = 43, p-value = 1
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.41674, df1 = 2, df2 = 45, p-value = 0.6617
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=309104&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.41674, df1 = 2, df2 = 45, p-value = 0.6617
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW] [ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0, df1 = 4, df2 = 43, p-value = 1
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW] [ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.41674, df1 = 2, df2 = 45, p-value = 0.6617
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309104&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309104&T=8

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.41674, df1 = 2, df2 = 45, p-value = 0.6617
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0, df1 = 4, df2 = 43, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.41674, df1 = 2, df2 = 45, p-value = 0.6617







Variance Inflation Factors (Multicollinearity)
> vif
      Politiek DeelVanDeSouth 
      1.097143       1.097143 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      Politiek DeelVanDeSouth 
      1.097143       1.097143 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=309104&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
      Politiek DeelVanDeSouth 
      1.097143       1.097143 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309104&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309104&T=9

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Variance Inflation Factors (Multicollinearity)
> vif
      Politiek DeelVanDeSouth 
      1.097143       1.097143 



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')